Beamforming techniques are considered as essential parts to compensate the severe path loss in millimeter-wave (mmWave) communications by adopting large antenna arrays and formulating narrow beams to obtain satisfactory received powers. However, performing accurate beam alignment over such narrow beams for efficient link configuration by traditional beam selection approaches, mainly relied on channel state information, typically impose significant latency and computing overheads, which is often infeasible in vehicle-to-vehicle (V2V) communications like highly dynamic scenarios. In contrast, utilizing out-of-band contextual information, such as vehicular position information, is a potential alternative to reduce such overheads. In this context, this paper presents a deep learning-based solution on utilizing the vehicular position information for predicting the optimal beams having sufficient mmWave received powers so that the best V2V line-of-sight links can be ensured proactively. After experimental evaluation of the proposed solution on real-world measured mmWave sensing and communications datasets, the results show that the solution can achieve up to 84.58% of received power of link status on average, which confirm a promising solution for beamforming in mmWave at 60 GHz enabled V2V communications.

翻译：波束赋形技术通过采用大型天线阵列并形成窄波束以获得足够的接收功率，被认为是补偿毫米波通信中严重路径损耗的关键手段。然而，传统波束选择方法主要依赖信道状态信息，在窄波束场景下实现精确波束对齐以高效配置链路时，通常会引入显著的延迟和计算开销，这在车辆对车辆通信等高度动态场景中往往不可行。相比之下，利用带外上下文信息（如车辆位置信息）是降低此类开销的潜在替代方案。在此背景下，本文提出一种基于深度学习的解决方案，利用车辆位置信息预测具有足够毫米波接收功率的最优波束，从而主动确保最优V2V视距链路。通过在真实毫米波感知与通信数据集上对所提方案进行实验评估，结果表明该方案平均可达链路状态接收功率的84.58%，验证了其在60 GHz毫米波V2V通信中实现波束赋形的可行性。